




'''simple docstring'''







import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCAmelCase_     						=				logging.get_logger(__name__)

lowerCAmelCase_     						=				{
    '''facebook/detr-resnet-50''': '''https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json''',
    # See all DETR models at https://huggingface.co/models?filter=detr
}

class 	_snake_case(			UpperCAmelCase      ):
 __snake_case:							List[Any]						=					'''detr'''
 __snake_case:							Union[str, Any]						=					['''past_key_values''']
 __snake_case:							str						=					{
     '''hidden_size''': '''d_model''',
     '''num_attention_heads''': '''encoder_attention_heads''',
 }






 def __init__(self       :  Dict    ,		a       :  List[Any]=True    ,		a       :  int=None    ,		a       :  Tuple=3    ,		a       :  str=1_00    ,		a       :  List[str]=6    ,		a       :  Optional[Any]=20_48    ,		a       :  Tuple=8    ,		a       :  str=6    ,		a       :  str=20_48    ,		a       :  Dict=8    ,		a       :  Union[str, Any]=0.0    ,		a       :  int=0.0    ,		a       :  Optional[Any]=True    ,		a       :  Tuple="relu"    ,		a       :  str=2_56    ,		a       :  Union[str, Any]=0.1    ,		a       :  Tuple=0.0    ,		a       :  Optional[int]=0.0    ,		a       :  List[str]=0.02    ,		a       :  Dict=1.0    ,		a       :  Tuple=False    ,		a       :  Any="sine"    ,		a       :  Any="resnet50"    ,		a       :  int=True    ,		a       :  List[str]=False    ,		a       :  Union[str, Any]=1    ,		a       :  str=5    ,		a       :  Any=2    ,		a       :  Any=1    ,		a       :  List[str]=1    ,		a       :  Any=5    ,		a       :  Dict=2    ,		a       :  Any=0.1    ,		**a       :  str    ,		)       ->   Optional[int]:






    """simple docstring"""


    if backbone_config is not None and use_timm_backbone:
       raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'    )

    if not use_timm_backbone:
       if backbone_config is None:
          logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'    )
          A__       =     CONFIG_MAPPING['resnet'](out_features=['stage4']    )
       elif isinstance(a    ,		a    ):
          A__       =     backbone_config.get('model_type'    )
          A__       =     CONFIG_MAPPING[backbone_model_type]
          A__       =     config_class.from_dict(a    )
       # set timm attributes to None
       A__  ,			A__  ,			A__       =     None, None, None

    A__       =     use_timm_backbone
    A__       =     backbone_config
    A__       =     num_channels
    A__       =     num_queries
    A__       =     d_model
    A__       =     encoder_ffn_dim
    A__       =     encoder_layers
    A__       =     encoder_attention_heads
    A__       =     decoder_ffn_dim
    A__       =     decoder_layers
    A__       =     decoder_attention_heads
    A__       =     dropout
    A__       =     attention_dropout
    A__       =     activation_dropout
    A__       =     activation_function
    A__       =     init_std
    A__       =     init_xavier_std
    A__       =     encoder_layerdrop
    A__       =     decoder_layerdrop
    A__       =     encoder_layers
    A__       =     auxiliary_loss
    A__       =     position_embedding_type
    A__       =     backbone
    A__       =     use_pretrained_backbone
    A__       =     dilation
    # Hungarian matcher
    A__       =     class_cost
    A__       =     bbox_cost
    A__       =     giou_cost
    # Loss coefficients
    A__       =     mask_loss_coefficient
    A__       =     dice_loss_coefficient
    A__       =     bbox_loss_coefficient
    A__       =     giou_loss_coefficient
    A__       =     eos_coefficient
    super().__init__(is_encoder_decoder=a    ,		**a    )






 @property
 def 						_UpperCamelCase (self       :  Optional[Any]    )       ->   int:






    """simple docstring"""


    return self.encoder_attention_heads






 @property
 def 						_UpperCamelCase (self       :  Any    )       ->   int:






    """simple docstring"""


    return self.d_model






 @classmethod
 def 						_UpperCamelCase (cls       :  Tuple    ,		a       :  PretrainedConfig    ,		**a       :  List[Any]    )       ->   Any:






    """simple docstring"""


    return cls(backbone_config=a    ,		**a    )






 def 						_UpperCamelCase (self       :  Dict    )       ->   Dict[str, any]:






    """simple docstring"""


    A__       =     copy.deepcopy(self.__dict__    )
    if output["backbone_config"] is not None:
       A__       =     self.backbone_config.to_dict()
    A__       =     self.__class__.model_type
    return output




class 	_snake_case(			UpperCAmelCase      ):
 __snake_case:							int						=					version.parse('''1.11'''      )






 @property
 def 						_UpperCamelCase (self       :  Optional[Any]    )       ->   Mapping[str, Mapping[int, str]]:






    """simple docstring"""


    return OrderedDict(
        [
            ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
            ('pixel_mask', {0: 'batch'}),
        ]    )






 @property
 def 						_UpperCamelCase (self       :  Optional[Any]    )       ->   float:






    """simple docstring"""


    return 1e-5






 @property
 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   int:






    """simple docstring"""


    return 12








'''simple docstring'''







from torch import nn







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   if act_fn in ["swish", "silu"]:
      return nn.SiLU()
   elif act_fn == "mish":
      return nn.Mish()
   elif act_fn == "gelu":
      return nn.GELU()
   else:
      raise ValueError(F"""Unsupported activation function: {act_fn}"""						)


